Abstract
Higher Education Institutions use social media as a marketing channel to attract and engage users so that the institution is promoted and thus a wide range of benefits can be achieved. These institutions are evaluated globally on various success parameters, being published in rankings. In this paper, we analyze the publishing strategies and compare the results with their overall ranking positions. The results show that there is a tendency to find a particular strategy in the top ranked universities. We also found cases where the strategies are less prominent and do not match the ranking positions. Copyright © 2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

Abstract
The proliferation of disinformation on social media platforms poses a significant challenge to the reliability of online information ecosystems and the protection of public discourse. This study investigates the role of emotional sequences in detecting intentionally misleading messages disseminated on social networks. To this end, we apply a methodological pipeline that combines semantic segmentation, automatic emotion recognition, and sequential pattern mining. Emotional sequences are extracted at the subsentence level, preserving each message's temporal order of emotional cues. Comparative analyses reveal that disinformation messages exhibit a higher prevalence of negative emotions, particularly fear, anger, and sadness, interspersed with neutral segments. Moreover, false messages frequently employ complex emotional progressions-alternating between high-intensity negative emotions and emotionally neutral passages-designed to capture attention and maximize engagement. In contrast, messages from reliable sources tend to follow simpler, more linear emotional trajectories, with a greater prevalence of positive emotions such as joy. Our dataset encompasses multiple categories of disinformation, enabling a fine-grained analysis of how emotional sequencing varies across different types of misleading content. Furthermore, we validate our approach by comparing it against a publicly available disinformation dataset, demonstrating the generalizability of our findings. The results highlight the importance of analyzing temporal emotional patterns to distinguish disinformation from verified content, reinforcing the value of integrating emotional sequences into machine learning pipelines to enhance disinformation detection. This work contributes to the growing body of research emphasizing the relationship between emotional manipulation and the virality of misleading content online.

Abstract
Automated assessment tools for programming assignments have become increasingly popular in computing education. These tools offer a cost-effective and highly available way to provide timely and consistent feedback to students. However, when evaluating a logically incorrect source code, there are some reasonable concerns about the formative gap in the feedback generated by such tools compared to that of human teaching assistants. A teaching assistant either pinpoints logical errors, describes how the program fails to perform the proposed task, or suggests possible ways to fix mistakes without revealing the correct code. On the other hand, automated assessment tools typically return a measure of the program's correctness, possibly backed by failing test cases and, only in a few cases, fixes to the program. In this paper, we introduce a tool, AsanasAssist, to generate formative feedback messages to students to repair functionality mistakes in the submitted source code based on the most similar algorithmic strategy solution. These suggestions are delivered with incremental levels of detail according to the student's needs, from identifying the block containing the error to displaying the correct source code. Furthermore, we evaluate how well the automatically generated messages provided by AsanasAssist match those provided by a human teaching assistant. The results demonstrate that the tool achieves feedback comparable to that of a human grader while being able to provide it just in time.

Abstract

Abstract
In today's competitive higher education sector, institutions increasingly rely on international rankings to secure financial resources, attract top-tier talent, and elevate their global reputation. Simultaneously, these universities have expanded their presence on social media, utilizing sophisticated posting strategies to disseminate information and boost recognition and engagement. This study examines the relationship between higher education institutions' (HEIs') rankings and their social media posting strategies. We gathered and analyzed publications from 18 HEIs featured in a consolidated ranking system, examining various features of their social media posts. To better understand these strategies, we categorized the posts into five predefined topics-engagement, research, image, society, and education. This categorization, combined with Long Short-Term Memory (LSTM) and a Random Forest (RF) algorithm, was utilized to predict social media output in the last five days of each month, achieving successful results. This paper further explores how variations in these social media strategies correlate with the rankings of HEIs. Our findings suggest a nuanced interaction between social media engagement and the perceived prestige of HEIs.

Abstract
As e-Learning systems have become gradually prevalent, forcing a (sometimes needed) physical distance between lecturers and their students, new methods need to emerge to fill this enlarging gap. Educators need, more than ever, systems capable of warning them (and the students) of situations that might create future problems for the learning process. The capacity to give and get feedback is naturally the best way to overcome this problem. However, in e-learning contexts, with dozens or hundreds of students, the solution becomes less simple. In this work we propose a system capable of continuously giving feedback on the performance of the students based on the interaction sequences they undertake with the LMS. This work innovates in what concerns the sequences of activity accesses together with the computation of the duration of these online learning activities, which are then encoded and fed into machine learning algorithms. We used a longitudinal experiment from five academic years. From our set of classifiers, the Random Forest obtained the best results for preventing low grades, with an accuracy of nearly 87%.